AutoMLExperimentExtension.SetSmacTuner Method
Definition
Important
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Set Microsoft.ML.AutoML.SmacTuner as tuner for hyper-parameter optimization. The performance of smac is in a large extend determined
by numberOfTrees, nMinForSpit and splitRatio, which are used to fit smac's inner
regressor.
public static Microsoft.ML.AutoML.AutoMLExperiment SetSmacTuner (this Microsoft.ML.AutoML.AutoMLExperiment experiment, int numberInitialPopulation = 20, int fitModelEveryNTrials = 10, int numberOfTrees = 10, int nMinForSpit = 2, float splitRatio = 0.8, int localSearchParentCount = 5, int numRandomEISearchConfigurations = 5000, double epsilon = 1E-05, int numNeighboursForNumericalParams = 4);static member SetSmacTuner : Microsoft.ML.AutoML.AutoMLExperiment * int * int * int * int * single * int * int * double * int -> Microsoft.ML.AutoML.AutoMLExperiment<Extension()>
Public Function SetSmacTuner (experiment As AutoMLExperiment, Optional numberInitialPopulation As Integer = 20, Optional fitModelEveryNTrials As Integer = 10, Optional numberOfTrees As Integer = 10, Optional nMinForSpit As Integer = 2, Optional splitRatio As Single = 0.8, Optional localSearchParentCount As Integer = 5, Optional numRandomEISearchConfigurations As Integer = 5000, Optional epsilon As Double = 1E-05, Optional numNeighboursForNumericalParams As Integer = 4) As AutoMLExperimentParameters
- experiment
- AutoMLExperiment
- numberInitialPopulation
- Int32
Number of points to use for random initialization.
- fitModelEveryNTrials
- Int32
re-fit random forests in smac for every N trials.
- numberOfTrees
- Int32
number of regression trees when fitting random forest.
- nMinForSpit
- Int32
minimum number of data points required to be in a node if it is to be split further for fitting random forest in smac.
- splitRatio
- Single
split ratio for fitting random forest in smac.
- localSearchParentCount
- Int32
Number of search parents to use for local search in maximizing EI acquisition function.
- numRandomEISearchConfigurations
- Int32
Number of random configurations when maximizing EI acquisition function.
- epsilon
- Double
the threshold to exit during maximizing EI acquisition function.
- numNeighboursForNumericalParams
- Int32
Number of neighbours to sample from when applying one-step mutation for generating new parameters.